[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5467/JKESS.2010.31.5.465

Half-hourly Rainfall Monitoring over the Indochina Area from MTSAT Infrared Measurements: Development of Rain Estimation Algorithm using an Artificial Neural Network

Thu, Nguyen Vinh (National Hydro-Meteorological Service)
Sohn, Byung-Ju (School of Earth and Environmental Sciences, Seoul National University)

Publication Information

Journal of the Korean earth science society / v.31, no.5, 2010 , pp. 465-474 More about this Journal

Abstract

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

Keywords

Rainfall retrieval; Indochina; IR remote sensing; MTSAT;

Citations & Related Records

Reference

1	Weng, F., Grody, N.C., Zhao, L., Meng, H., Kongli, C., Pellegrino, P., Qiu, S., and Dean, C., 2005, NOAA operational hydrological products derived from the Advanced Microwave Sounding Unit. IEEE Transactions on Geoscience and Remote Sensing, 43, 1036-1049. DOI
2	Xu, L., Gao, X., Sorooshian, S., and Arkin, P.A., 1999, A microwave infrared threshold technique to improve the GOES precipitation index. Journal of Applied Meteorology, 38, 569-579. DOI
3	Kummerow, C., Barnes, W., Kozo, T., Shiute, J., and Simpson, J., 1998, The Tropical Rainfall Measuring Mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15, 809-817. DOI
4	Kummerow, C. and Giglio, L., 1995, A method for combining passive microwave and infrared rainfall observations. Journal of Atmospheric and Oceanic Technology, 12, 33-45. DOI
5	Kummerow, C., Olsen, W.S., and Giglio, L., 1996, A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Transactions on Geoscience and Remote Sensing, 34, 1213-1232. DOI
6	Lippmann, R.P., 1987, An introduction to computing with neural network. IEEE ASSP Magazine, 4, 4-22. DOI
7	Oki, R., Furukawa, K., Shimizu, S., Suzuki, Y., Satoh, S., Hanado, H., Okamoto, K., and Nakamura, K., 1998, Preliminary results of TRMM: Part I, A comparison of PR with ground observations. Journal of Marine Technological Society., 32, 13-23.
8	Sapiano, M.R. and Arkin, P.A., 2009, An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. Journal of Hydrometeorology, 10, 149-166. DOI
9	Sohn, B.J., Han, H.-J., and Seo, E.-K., 2010, Validation of satellite-based high-resolution rainfall products over the Korean peninsula using data from a dense rain gauge network. Journal of Applied Meteorology and Climatology, 49, 701-714. DOI
10	Sorooshian, S., Hsu, K.L., Gao, X., Gupta, H.V., Imam, B., and Braithwaite, D., 2000, Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of American Meteorological Society, 81, 2035-2046. DOI
11	Huffman, G.J., Adler, R.F., Bolvin, D.T., Gu, G., Nelkin, E.J., Bowman, K.P., Hong, Y., Stocker, E.F., and Wolff, D.B., 2007, The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combinedsensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8, 38-55. DOI
12	Hsu, K.-L., Gao, X., Sorooshian, S., and Gupta, H.V., 1997, Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36, 1176-1190. DOI
13	Joyce, R.J., Janowiak, J.E., Arkin, P.A., and Xie, P., 2004, CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5, 487-503. DOI
14	Turk, F.J. and Miller, S.D., 2005, Toward improving estimates of remotely-sensed precipitation with MODIS/AMSR-E blended data techniques. IEEE Transactions on Geoscience and Remote Sensing, 43, 1059-1069. DOI
15	Adler, R.F., Negri, A.J., Keehn, P.R., and Hakkarinen, I.M., 1993, Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data. Journal of Applied Meteorology, 32, 335-356. DOI
16	Freeman, J.A. and Skapura, D.M., 1991, Neural networks: Algorithms, applications, and programming technique. Addison Wesley, USA, 305 p.
17	Huffman, G.J., Adler, R.F., Bolvin, D.T., and Nelkin, E.J., 1997, The global precipitation climatology project (GPCP) combined precipitation dataset. Bulletin of American Meteorological Society, 78, 5-20. DOI